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from typing import Dict, List, Any
import torch
from transformers import BertModel, BertTokenizerFast
class EndpointHandler():
def __init__(self, path_to_model: str = '.'):
# Preload all the elements you are going to need at inference.
# pseudo:
self.tokenizer = BertTokenizerFast.from_pretrained(path_to_model)
self.model = BertModel.from_pretrained(path_to_model)
self.model = self.model.eval()
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
This method is called whenever a request is made to the endpoint.
:param data: { inputs [str]: list of strings to be encoded }
:return: A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = self.tokenizer(data['inputs'], return_tensors = "pt", padding = True)
with torch.no_grad():
outputs = self.model(**inputs)
return outputs.pooler_output
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